A METHODOLOGICAL APPROACH TO THE CHARACTERIZATION OF BRAIN GLIOMAS, BY MEANS OF SEMI-AUTOMATIC MORPHOMETRIC ANALYSIS

Authors

  • Artur Dawid Surowka AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, al. A Mickiewicza 30, 30-059 Krakow, Poland
  • Dariusz Adamek Jagiellonian University, Faculty of Medicine, Department of Neuropathology, Chair of Pathomorphology, Krakow, Poland
  • Edyta Radwanska Jagiellonian University, Faculty of Medicine, Department of Neuropathology, Chair of Pathomorphology, Krakow, Poland
  • Marek Lankosz AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, al. A Mickiewicza 30, 30-059 Krakow, Poland
  • Magdalena Szczerbowska-Boruchowska AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, al. A Mickiewicza 30, 30-059 Krakow, Poland

DOI:

https://doi.org/10.5566/ias.1039

Keywords:

computer graphics, gliomas, grading, image processing, morphometry

Abstract

The aims of this paper were to present a reliable morphometric procedure for glioma analysis for preliminary prognosis and to develop a semi-automatic procedure that is easy to use. The data presented are important to the extent that they verify the reliability of the results by showing that they are consistent with the findings from more complicated automatic analytical tools. The objects for analysis were digital images of haematoxylin-eosin stained glioma samples. The overall analysis consisted of digital image analysis and the determination of morphometric parameters. Interestingly, an increase in the mean values of aspect ratio with increasing malignancy grade was found. Moreover, the morphometric parameters in relation to the histological origin of gliomas were examined and it was found that, the cellular nuclei of glioblastoma multiforme reveal the biggest mean values of aspect ratio compared with other gliomas.

References

Adamek D, Kałuża J (1993). Stereological aspects of pathology of gliomas: volume density of nuclei and volume corrected mitotic index. Acta Stereol. 12/1:65-70.

Bjornhagen V, Lindholm J, Auer G (1997). Analysis of nuclear DNA and morphometry and proliferating cell nuclear antigen in primary and metastatic malignant melanoma. Scand J Plast Reconstr Surg Hand Surg. 31:109-18.

Brat DJ, Prayson RA, Ryken TC, Olson JJ (2008). Diagnosis of malignant glioma: role of neuropathology. J Neurooncol. 89:287-311.

Cooper AD, Kong J, Gutman DA, Wang F, Choleti SR, Pan TC, et al. (2010) An Integrative Approach for In Silico Glioma Research. IEEE Trans Biomed Eng. 57(10): 2617–2621. doi:10.1109/TBME.2010.2060338.

Daumas-Duport C, Varlet P, Tucker ML, Beuvon F, Cervera P, Chodkiewicz JP (1988a). Grading of astrocytomas. A simple and reproducible method. Cancer 62:2152-65.

C. Daumas-Duport, B. Scheithauer, J. O’Fallon, P. Kelly (1988b). Grading of Astrocytomas. Simple and reproducible method. Cancer 62:2152-2165.

Decaestecker C, Camby I, Nagy N, Brotchi J, Kiss R, Salmon I (1998). Improving morphology-based malignancy grading schemes in astrocytic tumors by means of computer-assisted techniques. Brain Pathol 8:29-38

Dymecki J, Kulczycki J. Neuropatologia. Urban & Partner, Wrocław 2005.

Engelhard HH, Stelea A, Cochran EJ (2002). Oligodendroglioma: Pathology and Molecular Biology. Surg Neurol. 58:111-7.

Ferreira T, Rosband W (2012). ImageJ User Guide. IJ 1.46r. source: www.rsb.info.nih.gov/ij/gocs/guide/user-guide.pdf.

Glotsos D, Kalatzis I, Spyridonos P, Kostopoulos S, Daskalkis A, Anthanasiadis E, et.all. (2008) Improving accurancy in astrocytomas grading by integrading a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme. Comput Methods and Progr in Biomed. 90 251-261.

Gonzales MF (2004). Grading of Gliomas, J Clin Neurosci. 4(1):16-18.

Inagawa H, Ischizawa K, Hirose T (2007). Qualitative and Quantative Analysis of Cytologic Assesment of Astrocytoma , Oligodendroglioma and Oligoastrocytoma. Acta Cytol. 51:900-906.

Kayser K, Burger EG, Oberhilzer M, Goessner W (1988). Syntactic structural analyses in histopathology. Morpho in Cyto- and Histopathol. Berlin-Heidelberg-New York. Springer. 164-78.

Kolles H, Foerderer W, Bock R, Feiden W (1993). Combined Ki-67 and Feulgen stain for morphometric determination of the Ki-67 labelling index. Histochemistry. 100:293-6.

Kolles H, Von Wangenheim A, Vince GH, Niedermayer I, Feiden W (1995). Automated grading of astrocytomas based on histomorphometric analysis of Ki-67 and Feulgen stained paraffin sections. Classification results of neuronal networks and discriminant analysis. Anal Cell Pathol. 8: 101-16.

Kong J, Cooper L, Moreno C, Wang F, Kurc T, Saltz J, Brat D (2011). In Silico Analysis of Nuclei in Glioblastoma using Large-scale Microscopy Images Improves Prediction of Treatment Response. Conf Proc IEEE Eng Med Biol Soc. 87–90. doi:10.1109/IEMBS.2011.6089903.

Kros JM, Van Eden CG, Vissers CJ, Muldner AH, Van der Kwast TH (1992). Prognostic relevance of DNA flow cytometry in oligodendroglioma. Cancer 69: 1791-8.

Landini G (2006) . Quantitative analysis of the epithelial lining architecture in radicular cysts and odontogenic keratocysts. Head Face Med. 17;2:4.

Landini G, Perryer GJ (2009). Digital enhancement of haematoxylin- and eosin-stained histological images for red-green colour-blind observers. Microsc. 234(3):293-301. doi: 10.1111/j.1365-2818.2009.03174.x.

Leon SP, Folklerth RD, Black PM (1996). Microvessel density is a prognostic factor for patients with astroglial tumors. Cancer 77:362-72.

Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, (eds) (2007) WHO Classification of tumours of the central nervous system. IARC, Lyon.

Madhavan S, Zenklusen JC, Kotilarov Y, Sahni H, Fine HA, Buetow K (2009 ). Rembrandt: Helping personalized medicine become a reality through integrative translational research. Mol. Cancer Res vol. 2(no. 7):157–167.

Martin H, Voss K (1982a). Automated image analysis of glioblastomas and other gliomas. Acta neuropathol (Berl) 58:9-16.

Martin H, Voss K (1982b). Computerized Classification of Gliomas by Automated Microscope Picture Analysis. Acta Neuropathol (Berl58:261-268.

Martin H, Voss K (1982c). Computerized classification of gliomas by automated microscope picture analysis (AMPA). Acta Neuropathol (Berl) 58:261-8.

Martin H, Voss K, Hufnagl P, Froehlich K (1984a). Automated image analysis of gliomas. An objective and reproducible method for tumor grading. Acta Neuropathol (Berl 63:190-9.

Martin H, Guski M, Guski H (1984b). Karyometric investigations of meningeomas. Zentralbl Neurochir. 45: 289-98.

Nafe R, Schlote W, Schneider B (1999). Quantitative evaluation of nuclear pleromorphism in oligodendroglial tumors by means of Fourier-anslysis of nuclear outlines. Electron J Pathol. 5:994-07.

Nafe R, Schlote W, Schneider B (2000a). Shape analysis of tumor cell nuclei in epedymomas by means of Fourier analysis. Anal Quant Cytol Histol. 22:475-82.

Nafe R, Schneider B (2000b). Data analysis for comparative histometry in pathology- I. Classification procedures and other statistical methods. Electron J Pathol 6:002-3.txt.

Nafe R, Schlote W (2002b). Methods for shape analysis of two dimensional closed contours – a biologically important but widely neglected field in histopatology. Electron J Pathol 8:022-02.txt.

Nafe R, Schlote W (2002a). Densitometric Analysis of Tumor Cell Nuclei in low-grade and high-grade Astrocytomas. Electron J of Pathol and Histol. 8.3:023-02.pdf.

Nafe R, Schlote W (2003). Topometric analysis of diffuse astrocytoma. Anal Quant Cytol Histol. 25: 12-18.

Nafe R, Schlote W (2004b). Histomorphometry of brain tumors. Neuropathol and Appl Neurobiol. 30:315-328.

Nafe R, Schlote W, Schneider B (2005). Histomorphometry of tumor cell nuclei in astrocytomas using shape analysis, densitometry and topometric analysis. Neuropathol and Appl Neurobiol. 31:34-44.

NIH ImageJ software, source: www.rsb.info.nih.gov/ij

Ohgaki H , Kleihues P (2005). Epidemiology and etiology of gliomas. Acta Neuropathol. 109: 93-108.

Prodanov D, Nagelkerke N, Marani EJ (2007). Spatial clustering analysis in neuroanatomy: applications of different approaches to motor nerve fiber distribution, Neurosci Methods. 160(1):93-108. Epub 2006 Oct 17.

Ricco R, Serio G, Caniglia DM, Cimmino A, Lettini T, Lozupone A, Delfino VP (1994). Size and shape evaluation of astrocytoma nuclei with the shape analytical morphometry software system. Anal Quant Cytol Histol. 16:345-50.

Sallinen SL, Helen PT, Rantala IS, Rautiainen E, Helin HJ, et al. (2000). Grading of diffusely infiltrating astrocytomas by quantitative histopatology, cell proliferation and image cytometric DNA analysis. Neuropathol Appl Neurobiol. 26: 319-31.

Saito A, Yoshii Y, Nose T (1994). Image Analysis of nuclear DNA content and morphometric characteristics of the tumor cells in human astrocytomas. Brain tumor Pathol. 11:143-6.

Schiffer D, Giordana MT, Mauro A, Migheli A (1983). Glial fibrillary acid protein (GFAP) in human cerebral tumors. An immunohistochemical study. Tumori. 69(2):95-104.

Schiffer D, Chio A, Giordana MT, Mauro A, Mighell A, Vigltant MC (1989). The vascular response to tumor infiltration in malignant gliomas. Morphometric and reconstruction study. Acta Neuropathol 77:369-78.

TCGA Consortium (2008) .Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. vol. 455:1061–1068.

The Cancer Genome Atlas (TCGA) publications policy, source: http://cancergenome.nih.gov/abouttcga/policies/publicationguidelines.

Ullen H, Falkmer UG, Collins VP, Auer GU (1991). Methodologic aspects of nuclear DNA assessment of gliomas with astrocytic and/or oligodendrocytic differentiation. Anal Quant Cytol Histol. 13:168-76.

Watkins S, Sontheimer H (2012). Unique biology of gliomas: challenges and opportunities. Trends in Neurosci. vol. 35, no. 9.

Wen PY, Kesari S (2004). Malignant Gliomas. Curr Neurol and Neurosci Rep. 4:218-227.

Wesseling P, van der Laak JA, de Leeuw H, Ruiter DJ, Burger PC (1994). Quantitative immunohistological analysis of the microvasculature in untreated human glioblastoma multiforme. Computer-assisted image analysis of whole tumor or sections. J Neurosurg. 81: 902-9.

Van den Bent MJ, Reni M, Gatta G, Vecht C (2008). Oligodendroglioma. Crit Rev in Oncol/Hematol. 66 262-272.

Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y, et al., and The Cancer Genome Atlas Research Network (2010 ). Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. vol. 17(no. 1):98–110.

Vilanova JR, Burgos-Bretones J, Simon R, Aguierre-Urizar JM, J.Rivera-Pomar JM (1982). Hypothetical evolution of necrosis in glioblastomas. Pathol Res Pract. 173:283-93.

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Published

2014-05-23

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Section

Original Research Paper

How to Cite

Surowka, A. D., Adamek, D., Radwanska, E., Lankosz, M., & Szczerbowska-Boruchowska, M. (2014). A METHODOLOGICAL APPROACH TO THE CHARACTERIZATION OF BRAIN GLIOMAS, BY MEANS OF SEMI-AUTOMATIC MORPHOMETRIC ANALYSIS. Image Analysis and Stereology, 33(3), 201-218. https://doi.org/10.5566/ias.1039